Forecasting performance of workforce reskilling programmes

TL;DR

This paper introduces a new estimation method for workforce reskilling programme success rates, focusing on fundamental labour market factors rather than historical data, and demonstrates improved accuracy during market shocks.

Contribution

The paper presents a novel estimation approach based on demand and supply fundamentals, outperforming traditional historical-based methods during labour market shocks.

Findings

Average error of 3.9% in success rate estimation

Model outperforms benchmark by 53%

Effective during Brexit and Covid-19 shocks

Abstract

Estimating success rates for programmes aiming to reintegrate theunemployed into the workforce is essential for good stewardship of publicfinances. At the current moment, the methods used for this task arebased on the historical performance of comparable programmes. In lightof Brexit and Covid-19 simultaneously causing a shock to the labourmarket in the UK we developed an estimation method that is basedon fundamental factors involved - workforce demand and supply - asopposed to the historical values which are quickly becoming irrelevant.With an average error of 3.9% of the re-integration success rate, ourmodel outperforms the best benchmark known to us by 53%